pa ass !!!.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d566105",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rc(\"font\",size=14)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split#splitting data arr into two subsets\n",
    "sns.set(style=\"white\")\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "from sklearn.metrics import accuracy_score,classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve \n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5566912b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>83.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>125.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.6</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>Female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5110 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0       Male  67.0             0              1          Yes        Private   \n",
       "1     Female  61.0             0              0          Yes  Self-employed   \n",
       "2       Male  80.0             0              1          Yes        Private   \n",
       "3     Female  49.0             0              0          Yes        Private   \n",
       "4     Female  79.0             1              0          Yes  Self-employed   \n",
       "...      ...   ...           ...            ...          ...            ...   \n",
       "5105  Female  80.0             1              0          Yes        Private   \n",
       "5106  Female  81.0             0              0          Yes  Self-employed   \n",
       "5107  Female  35.0             0              0          Yes  Self-employed   \n",
       "5108    Male  51.0             0              0          Yes        Private   \n",
       "5109  Female  44.0             0              0          Yes       Govt_job   \n",
       "\n",
       "     Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0             Urban             228.69  36.6  formerly smoked       1  \n",
       "1             Rural             202.21   NaN     never smoked       1  \n",
       "2             Rural             105.92  32.5     never smoked       1  \n",
       "3             Urban             171.23  34.4           smokes       1  \n",
       "4             Rural             174.12  24.0     never smoked       1  \n",
       "...             ...                ...   ...              ...     ...  \n",
       "5105          Urban              83.75   NaN     never smoked       0  \n",
       "5106          Urban             125.20  40.0     never smoked       0  \n",
       "5107          Rural              82.99  30.6     never smoked       0  \n",
       "5108          Rural             166.29  25.6  formerly smoked       0  \n",
       "5109          Urban              85.28  26.2          Unknown       0  \n",
       "\n",
       "[5110 rows x 11 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stroke=pd.read_csv('stroke prediction.csv')\n",
    "stroke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "394f6290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5110, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stroke.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57fce27c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
       "       'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
       "       'smoking_status', 'stroke'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stroke.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3ed3e236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                 0\n",
       "age                    0\n",
       "hypertension           0\n",
       "heart_disease          0\n",
       "ever_married           0\n",
       "work_type              0\n",
       "Residence_type         0\n",
       "avg_glucose_level      0\n",
       "bmi                  201\n",
       "smoking_status         0\n",
       "stroke                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "stroke.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2df30e17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                  0\n",
       "age                     0\n",
       "hypertension         4612\n",
       "heart_disease        4834\n",
       "ever_married            0\n",
       "work_type               0\n",
       "Residence_type          0\n",
       "avg_glucose_level       0\n",
       "bmi                     0\n",
       "smoking_status          0\n",
       "stroke               4861\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stroke[stroke==0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a619d7d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rajesh\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='stroke', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(stroke['stroke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ace32b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#filling null values with the mean\n",
    "stroke['bmi'].fillna(stroke['bmi'].mean(), inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4a9abee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#filling null values with mode\n",
    "stroke['smoking_status'].fillna(stroke['smoking_status'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0b61f73e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               0\n",
       "age                  0\n",
       "hypertension         0\n",
       "heart_disease        0\n",
       "ever_married         0\n",
       "work_type            0\n",
       "Residence_type       0\n",
       "avg_glucose_level    0\n",
       "bmi                  0\n",
       "smoking_status       0\n",
       "stroke               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the data\n",
    "stroke.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "809c5298",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "      <td>5110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.226614</td>\n",
       "      <td>0.097456</td>\n",
       "      <td>0.054012</td>\n",
       "      <td>106.147677</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>0.048728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.612647</td>\n",
       "      <td>0.296607</td>\n",
       "      <td>0.226063</td>\n",
       "      <td>45.283560</td>\n",
       "      <td>7.698018</td>\n",
       "      <td>0.215320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.120000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.245000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.885000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114.090000</td>\n",
       "      <td>32.800000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>271.740000</td>\n",
       "      <td>97.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age  hypertension  heart_disease  avg_glucose_level  \\\n",
       "count  5110.000000   5110.000000    5110.000000        5110.000000   \n",
       "mean     43.226614      0.097456       0.054012         106.147677   \n",
       "std      22.612647      0.296607       0.226063          45.283560   \n",
       "min       0.080000      0.000000       0.000000          55.120000   \n",
       "25%      25.000000      0.000000       0.000000          77.245000   \n",
       "50%      45.000000      0.000000       0.000000          91.885000   \n",
       "75%      61.000000      0.000000       0.000000         114.090000   \n",
       "max      82.000000      1.000000       1.000000         271.740000   \n",
       "\n",
       "               bmi       stroke  \n",
       "count  5110.000000  5110.000000  \n",
       "mean     28.893237     0.048728  \n",
       "std       7.698018     0.215320  \n",
       "min      10.300000     0.000000  \n",
       "25%      23.800000     0.000000  \n",
       "50%      28.400000     0.000000  \n",
       "75%      32.800000     0.000000  \n",
       "max      97.600000     1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stroke.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4da22aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5110 entries, 0 to 5109\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             5110 non-null   object \n",
      " 1   age                5110 non-null   float64\n",
      " 2   hypertension       5110 non-null   int64  \n",
      " 3   heart_disease      5110 non-null   int64  \n",
      " 4   ever_married       5110 non-null   object \n",
      " 5   work_type          5110 non-null   object \n",
      " 6   Residence_type     5110 non-null   object \n",
      " 7   avg_glucose_level  5110 non-null   float64\n",
      " 8   bmi                5110 non-null   float64\n",
      " 9   smoking_status     5110 non-null   object \n",
      " 10  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 439.3+ KB\n"
     ]
    }
   ],
   "source": [
    "stroke.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "611735cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rajesh\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='avg_glucose_level', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(stroke['avg_glucose_level'], bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e0bbd027",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rajesh\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bmi', ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(stroke['bmi'], bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1bc6e9de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rajesh\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(stroke['age'], bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29c0aeef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2618dd251c0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#does age has impact on strokes  SO ans->yes\n",
    "#chances of stroke increases with increases in age\n",
    "stroke.loc[stroke['stroke'] == 0,'age'].hist(label='No Stroke')\n",
    "stroke.loc[stroke['stroke'] == 1,'age'].hist(label='Heart Stroke')\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('Heart Stroke')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b0934f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2618db60310>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#chances of stroke more with bmi 20-40\n",
    "stroke.loc[stroke['stroke'] == 0,'bmi'].hist(label='No Stroke')\n",
    "stroke.loc[stroke['stroke'] == 1,'bmi'].hist(label='Heart Stroke')\n",
    "plt.xlabel('bmi')\n",
    "plt.ylabel('Heart Stroke')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fb41d5d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2618dc15160>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#chances of stroke high with glucose levels in range of 70-100\n",
    "stroke.loc[stroke['stroke'] == 0,'avg_glucose_level'].hist(label='No Stroke')\n",
    "stroke.loc[stroke['stroke'] == 1,'avg_glucose_level'].hist(label='Heart Stroke')\n",
    "plt.xlabel('Glucose Level')\n",
    "plt.ylabel('Heart Stroke')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b406bd75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2853.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender  Female    Male  Other\n",
       "stroke                       \n",
       "0       2853.0  2007.0    1.0\n",
       "1        141.0   108.0    NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#married females have more chances of heart stroke than married males\n",
    "pd.pivot_table(stroke, index= 'stroke', columns='gender', values='ever_married', aggfunc= 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5879f2f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2853.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender  Female    Male  Other\n",
       "stroke                       \n",
       "0       2853.0  2007.0    1.0\n",
       "1        141.0   108.0    NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#females with hypertension has more chance of heart stroke than males having hypertension problem\n",
    "pd.pivot_table(stroke, index= 'stroke', columns='gender', values='hypertension', aggfunc= 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5e20d81f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2853.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender  Female    Male  Other\n",
       "stroke                       \n",
       "0       2853.0  2007.0    1.0\n",
       "1        141.0   108.0    NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#females with heart disease has more chances of stroke\n",
    "pd.pivot_table(stroke, index= 'stroke', columns='gender', values='heart_disease', aggfunc= 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9385cd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>work_type</th>\n",
       "      <th>Govt_job</th>\n",
       "      <th>Never_worked</th>\n",
       "      <th>Private</th>\n",
       "      <th>Self-employed</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>624.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>754.0</td>\n",
       "      <td>685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "work_type  Govt_job  Never_worked  Private  Self-employed  children\n",
       "stroke                                                             \n",
       "0             624.0          22.0   2776.0          754.0     685.0\n",
       "1              33.0           NaN    149.0           65.0       2.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#people having private jobs and has a habit of smoking has more chance of heart stroke \n",
    "pd.pivot_table(stroke, index= 'stroke', columns='work_type', values='smoking_status', aggfunc= 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "681bedbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ,  1.2]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#as age incraeses gender does not play any role in heart stroke\n",
    "sns.scatterplot(x= 'stroke', y='age', hue='gender', sizes= (15,200), data=stroke)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f8fbd1c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ,  1.2]),\n",
       " [Text(-0.2, 0, '−0.2'),\n",
       "  Text(0.0, 0, '0.0'),\n",
       "  Text(0.2, 0, '0.2'),\n",
       "  Text(0.4000000000000001, 0, '0.4'),\n",
       "  Text(0.6000000000000001, 0, '0.6'),\n",
       "  Text(0.8, 0, '0.8'),\n",
       "  Text(1.0000000000000002, 0, '1.0'),\n",
       "  Text(1.2000000000000002, 0, '1.2')])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 439.55x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#can't say that marriage plays a role in heart stroke as people generally marry after the age of 25years\n",
    "sns.relplot(x= 'stroke', y='age', hue= 'ever_married', sizes= (15,200), data=stroke)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c6e7c26b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,  50., 100., 150., 200., 250., 300.]),\n",
       " [Text(0.0, 0, '0'),\n",
       "  Text(50.0, 0, '50'),\n",
       "  Text(100.0, 0, '100'),\n",
       "  Text(150.0, 0, '150'),\n",
       "  Text(200.0, 0, '200'),\n",
       "  Text(250.0, 0, '250'),\n",
       "  Text(300.0, 0, '300')])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2016x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 405.725x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#with age glucose level increases which increases the chances of stroke\n",
    "plt.figure(figsize=(28,20))\n",
    "sns.relplot(x= 'avg_glucose_level', y='age', hue= 'stroke', sizes= (15,200), data=stroke)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5e3a7fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4861\n",
       "1     249\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stroke['stroke'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "778e9224",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ENCODING THE DATASETS\n",
    "from sklearn import preprocessing \n",
    "encoder = preprocessing.LabelEncoder()\n",
    "    for i in stroke.columns:\n",
    "        if isinstance(stroke[i][0], str):\n",
    "            stroke[i] = encoder.fit_transform(stroke[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "feb10583",
   "metadata": {},
   "outputs": [],
   "source": [
    "#STANDARDIZING\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "scalar = StandardScaler() \n",
    "scalar.fit(stroke) \n",
    "scaled_data = scalar.transform(stroke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "452137b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5110, 10)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a model\n",
    "X= stroke.drop('stroke', axis=1)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5a1fbafd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5110,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y= stroke['stroke']\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6f2a52f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "np.random.seed(0)\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=.3, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b543e575",
   "metadata": {},
   "outputs": [],
   "source": [
    "log = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "542d3904",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rajesh\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0c38d8da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9496785015376014"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b22cee8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#this model shows that it is overfit which is considering the high data so that we have to balance the data by some models(i.e) logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cf30323",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1e70d24f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to retain the original data, we craeted a copy of the dataset\n",
    "stroke_copy= stroke.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "33c20645",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>202.21</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease  ever_married  work_type  \\\n",
       "0       1  67.0             0              1             1          2   \n",
       "1       0  61.0             0              0             1          3   \n",
       "2       1  80.0             0              1             1          2   \n",
       "3       0  49.0             0              0             1          2   \n",
       "4       0  79.0             1              0             1          3   \n",
       "\n",
       "   Residence_type  avg_glucose_level        bmi  smoking_status  stroke  \n",
       "0               1             228.69  36.600000               1       1  \n",
       "1               0             202.21  28.893237               2       1  \n",
       "2               0             105.92  32.500000               2       1  \n",
       "3               1             171.23  34.400000               3       1  \n",
       "4               0             174.12  24.000000               2       1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stroke_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "22be483b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4861\n",
       "1     249\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a list of data values which is more in number to make a balance data\n",
    "li = list(stroke_copy[stroke_copy.stroke == 0].sample(n=2000).index)\n",
    "stroke_copy['stroke'].value_counts() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6b596e98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>228.69</td>\n",
       "      <td>36.600000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>5105</td>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>83.75</td>\n",
       "      <td>28.893237</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>5106</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>125.20</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>5107</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>5108</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>5109</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5110 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  gender   age  hypertension  heart_disease  ever_married  \\\n",
       "0         0       1  67.0             0              1             1   \n",
       "1         1       0  61.0             0              0             1   \n",
       "2         2       1  80.0             0              1             1   \n",
       "3         3       0  49.0             0              0             1   \n",
       "4         4       0  79.0             1              0             1   \n",
       "...     ...     ...   ...           ...            ...           ...   \n",
       "5105   5105       0  80.0             1              0             1   \n",
       "5106   5106       0  81.0             0              0             1   \n",
       "5107   5107       0  35.0             0              0             1   \n",
       "5108   5108       1  51.0             0              0             1   \n",
       "5109   5109       0  44.0             0              0             1   \n",
       "\n",
       "      work_type  Residence_type  avg_glucose_level        bmi  smoking_status  \\\n",
       "0             2               1             228.69  36.600000               1   \n",
       "1             3               0             202.21  28.893237               2   \n",
       "2             2               0             105.92  32.500000               2   \n",
       "3             2               1             171.23  34.400000               3   \n",
       "4             3               0             174.12  24.000000               2   \n",
       "...         ...             ...                ...        ...             ...   \n",
       "5105          2               1              83.75  28.893237               2   \n",
       "5106          3               1             125.20  40.000000               2   \n",
       "5107          3               0              82.99  30.600000               2   \n",
       "5108          2               0             166.29  25.600000               1   \n",
       "5109          0               1              85.28  26.200000               0   \n",
       "\n",
       "      stroke  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "...      ...  \n",
       "5105       0  \n",
       "5106       0  \n",
       "5107       0  \n",
       "5108       0  \n",
       "5109       0  \n",
       "\n",
       "[5110 rows x 12 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stroke = pd.DataFrame(stroke)\n",
    "stroke.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5f27861d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5110, 10)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = stroke.drop(['stroke'], axis=1).values \n",
    "y = stroke['stroke'].values\n",
    "X.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "acb2b734",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3,random_state=1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "abebbd39",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rajesh\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4fb35f8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9445531637312459"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the accuracy has dropped\n",
    "log.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d1ebf735",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the output with Logistic\n",
    "y_underlog = log.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2e2dce56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score of the model is: 95.04892367906066\n",
      "The F1 score of the model is: 3.8022813688212933\n",
      "The recall score of the model is: 2.0080321285140563\n",
      "The confusion matrix of the model is: [[4852    9]\n",
      " [ 244    5]]\n",
      "The classification report of logistic model is:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.97      4861\n",
      "           1       0.36      0.02      0.04       249\n",
      "\n",
      "    accuracy                           0.95      5110\n",
      "   macro avg       0.65      0.51      0.51      5110\n",
      "weighted avg       0.92      0.95      0.93      5110\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('The accuracy score of the model is:', accuracy_score(y,y_underlog)*100)\n",
    "print('The F1 score of the model is:', f1_score(y, y_underlog)*100)\n",
    "print('The recall score of the model is:', recall_score(y, y_underlog)*100)\n",
    "print('The confusion matrix of the model is:', confusion_matrix(y, y_underlog))\n",
    "print('The classification report of logistic model is:', classification_report(y, y_underlog))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8a78ba36",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_log= confusion_matrix(y, y_underlog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6c9bcdaf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Logistic Regression Confusion Matrix'}>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y/VHPwb9cISkla8B/PH0Vnd5AqZJmKPnMqFMnTwDw7oCBlDAzyzK//7vpz537Yf8+BbdaPBSrj3wzRnTHoX51Pl5zINuwZIiK+ZOqNlY4NKjOwRP/yzTPzrYKABHR8ZmmN7atikaj5krYvWw7LLKzctsxjv92g1uRD7Kdb15Ciyabx1gXVPS99Ctow0aNs51fo2YtAG7cCCU5OTnTQ0ql3BWbQDWyrcKM/3Tj2s1oFq0JxsWxYY7Lrt91klZNavH+sC6c/D2ME7/dAKBpg+r4jE9/IP2qbccyrZPxce/qzXt0aFWfvt1bUKd6BRKTUzl5Poz1u07yKDFzh8SlkCguhUTlWIdrhyZoNGr0eiNCpclxuedlzOaR1wAGgx5If+54TEw0tWrVVnzbr6piESi1WsWq+QMpodUw3jcInT77N1KG9btOUtG6DDP/053g1e9xK/IheoORejVtSE7V4/PlPr7aciTTOvZ/9fD1cLbnnTczj+TQq6MDU4a60G/aGs5cvJWnmktZmDFvXHp4D564TFfnpnnc22erVq06N2+GERoaQvM3WmSZf/OvZ7YDzz+SXzFVLO6hpgx2oaV9LVZvP86pP27maZ0bt2MJv/sQtVqNbY2KNKxTGY1GzeMnKZk6DzLY/3WFUqtVzPxsJ7W6zOa11lPoOPQzjp0LpVL5sny3bCzVK1k9c9tqtYpvPhqKbY2KJKfomPelsvcyzn8N2bNx/bosQ4WmpaWx4Zu1pr8Ner2i237VFdkrVFhYGMHBwURHR6NWq7GxscHZ2ZkmTZrkuy3vsT2JuBfH3IC9eVp+zugezB3nSszDBAbPXMeB45fRaNT0aGfPJx+8TYBXf+rVtGHmZztN6+w/conImD/ZtPd0pp7BMxdv4TbuS45seJ83GtVgxsg3eW/h1hy3rdGoWec3hF6d0q9IUxfv4NrN6ByXfx6Dhw5nz66dRETcYfyYkbw/YxZ169YjKjKCz5d+RsSdO1hYWJCSkoJWW2TfIkVSkbxCBQYGMm3aNACaNGliGoVj7ty5rFu3Lt/tlbQw472PtpGYlPrMZevXqsSc0T0wGtPoN20N3x46z5NkHQmJKWw7eA63cV+i1xt5b1BnmtSvZlrvqy1HGDVvU6YwZdAbjCzb9BMAru1zHjGklIUZ25eMNn1knBewlw27T+V3d5+pYkUbln25gtdes+LXs2fo79mHls3scXfrwZlTJ1n06Wem8ahKl846WJ6UsyJ5+tm4cSO7d+/O0rs0fPhw+vTpw4gRI/LV3rYD57L01uWkd+emaDRqfj5zPdv7nUshUXx/7BIeLs14q0vzXDsVnvbH9UggvUteq1Vn+ZZFeavS7A4YR0v7WgDMWbqLpRt/ylPbz6NZ8zfYvf8A27cGceniHwDY2TXi7b79qFChAo8fPwYwDVUj5U2RDJRWq8120LWUlBRKlCiR7/b69WhJvx5Zh3wEqFm1PMkX0p/K2qDnPGpUSR+I+Xp4TI7thd6OBTAtm8HCvAQpqdnfc2SMPmI0pmUJU/VKVuxfMZEGtStjMBiZuHDrC7ky/ZO1tTVjx2d9qu6V/13GaDRS0caGsq+99sLreJUUyUCNHTsWDw8P2rRpQ8WKFVGpVMTGxnL69GmmTp2a7/ZOXgjLMs2qbCka2VYhJVXP+Svp/1ibqtPz+K+u7SoVch7GJiNICU9SAGhSvxqH10/DspQ5dbp5ce9+1qEjm9avDmQNaoVylny/chL1a1UiJVXPkFnr2XfkYr73MT/O/3aOS5cu0vyNFjg4ZO09PHb0CACtWrV+oXW8iopkoHr16kXr1q05deoUsbGxpKWl0bJlSyZNmkSlSpXy3Z7LiKVZpvVwtmfnF2OJeZiQaf6x30KZNqwrXZzsqFrxNe7+IxwVy1nSpU364NEnfku/X7p+KwaD0QjAQLfW+K//b6Z11GoVEwZ0BGDnfy+YpqtUKgI/+Q/1a1UiKVnH21NWcuRsSL73L78uXfyDJf6f0L1HTz7xz/zaJCYmsmNbeqdJ337vvvBaXjVFslMC0seL9fDwYPTo0aYr1vOEKb+CT1zhtyt3KF3SnG+XjaVeTRvTvBpVrNm2ZBTlrUrzvxt32X04/d5DpzewYutRALzG9KRPl2amdcpaWrDObyitmtQiKiaegMC/B3Ee5tGG9n99/Wjiwi3/SpgAOnXuQokSJTh08AAHvv97jNwH9+8zZdJ4Hjy4j3P7DrzRIvuPyVLOisWAa9mNvpFxhbp992GW30PVqFKO71dOom4NG9LS0rgeHoNapaJuDRs0GjW3Ih/gOu7LTF8Z0mrVbPUfZfrJx93YP7n3IAG72pUpVdKM+/GP6TkmINNXnn7f6U2D2pXR6Q2cu3w7131o0cQWJQ9UUOAmFn/kB0DVatUoU6YsN8NuoNfradS4MWvWbcTSUtkevuIw+kYx2MX8u3MvHqcBnzBxYEc8XJpR9/X0q9S1W9HsOfwHX2w6nOVrRAZDGn2nrmaIuyNDejtiX68qjW2rEBkTzw/HLvPpukM8iE80LV/eqjQNaqf/hMSshBan5ra51qQzgpKnvgEDB1OpUmUCN23g6pX/cT82lho1a9HT1Y1BQ4aZus2l/Cm2V6iXjRwf6uVQZO+hJOllJAMlSQqSgZIkBclASZKCZKAkSUEyUJKkIBkoSVKQDJQkKUgGSpIUJAMlSQqSgZIkBclASZKCZKAkSUEyUJKkIBkoSVKQDJQkKahY/MAwPslY2CUUWEkz5QcL+LfJHxhKkpQvMlCSpCAZKElSkAyUJClIBkqSFCQDJUkKkoGSJAXJQEmSgmSgJElBMlCSpCAZKElSkAyUJClIBkqSFCQDJUkKKgZfqH+28Fs32bxhLefOnuHhg/uYm1tQr34DevfxpIdb72eub9DrGTqwL2GhIXy15htatMzbYM8b161hecBSevbyYJ7vRwXdjXz78894OrR1zHWZUaPHMnFy/gcKL66KfaCOH/0Z75nTSE1NxdzcnJq16hAX94AL589x4fw5Tp08zoKFn6BSqXJsY/3aVYSF5m983Nvht1i7enlByy+Q0JD0mq2srKhVu062y1SpWu3fLOmlV6wD9fDhA3y8ZpCamor7W32ZOn0WFiVLAnD05x9ZMHc2hw58T2N7B/oNGJxtGzdCQ9iwdnW+tpuWlobffG9SU1MLvA8FERJyHYA3e7ox22tuodbyqijW91B7d31L0pMnNLBrxEwvH1OYADp06sL4SekfdbYGbsx2faPRyML53ghAq837uWnH1kAu/XEB80Iex/bGX1co27p1C7WOV0mxDtT5c78C0LFzF9TqrC9FW+cOANy7G0VCwqMs8wM3rufqlcsMGDSM0nkcMf1uVCQrv/ycqtWq49rL4/mLV0DoXx9T69atV6h1vEqKdaDGjH+PuQs+okMnl2znpyT/PdK70ZD5uRS3w2+xdtVXvF6jJiPHTsjT9oQQfOQ7l+TkZGZ5zy/UkdaFEITdCAXA1lZeoZRSrO+h7B2aYu/QNMf5x44cBqBcOWusypUzTRdCsHDBXHQ6HbPmLsDc3DxP29uzcwfnzp7Bzb0PrR2dOH3yRMF2oAAiIyJISkqiQoWKxMU9ZP26r7l+7SoA9Rs05C3PvtSsWavQ6ntZFetA5ebhg/ts3rAOgG49XDP18m3fspmLv5/H4+2+ee4ij42JJuBzf6zLl+e9aTNeSM35kfFxLzHxMW979MJo/PsKfPKXE2zeuIFZXt70fad/YZX4UirWH/lykpycxIxpk3j8OAErq3IMGTHKNC/jHqhiRRsmTp6e5zYX+c3nSWIi02d6U7bsay+i7HwJuX4NgJSUFN7y7Mue/Qc4d+ESe38IxvOdfhgMehb6zufokZ8Lt9CXjLxC/UNS0hOmT57A/y5dRKPRMH/hYsqXr2Ca/5HvPJKTk5m/cDGWZcrkqc0D+/dy8sQx2nfsTOeu3V9U6fli16gxnu/0o27d+rw7cJBpes2atZjr44tWW4KtQZtZ4r+YDh07FWKlL5ciG6i7d+/mOr9q1aqKbzM+Lo73J4/jyuVLqNVqvOcvxNGpnWn+7p07OHf2NC5du9OhU5c8tfnw4QOW+n+MpWUZPpg9T/Gan1eHjp1yDcrI0WPYGrSZ8Fu3uH07XN5P5VGRDdSYMWMIDw/HxsaGfz7cVqVS8dNPPym6vajICCaPH0lkRAQarRYf34/p1sPVND82NoYvl/pTtmxZps30ynO7/h/7kfDoEbO851PRxkbRml+kihVtsC5fnriHD7l3964MVB4V2UcxJyYmMmDAAHx8fGjRokWB2nrWo5hDQ64zZcIoHj54gIVFST76ZAlOf/0bVIb9e3fh55P3IP1nzHhGjZ2IY/NGeV6ncpWq7P7hx2znvYhHMev1etRqNRpN9m13dG5DfFwc6zZspkXLVgXeXnF4FHOR3UVLS0v8/PzYsWNHgQOVmzu3w3lv3Eji4x5StmxZPvtiJU2aNsuynLV1eRyavZFjO/+7fBGjwYBt3XqUtixD5cpVAHJd597dKO7HxlDOujyv16hJhQoVclxWad1cOhATHc2iTz6jh6tblvmxsTHEx8UB5Pg9PymrIhsoAAcHBxwcHF5Y+ynJyXwwZQLxcQ+xsipHwKp11KvfINtlndq1x6ld+xzb6t7JiUd//sm0mV6ZutJXr9+c4zpfLPmEoE3f0Kat87/+bXNb27rEREezb+/ubAO18Zv1ALRs1Zry5cv/q7W9zIp1t/n6tau4HX4LtVrNwk+X5himV9HQ4f8B4JcTx1m29DP0Oh2Q/v3EDevXsnnjN2g0GiZPy/s/DUhF/Ar1Iul0Or7btgUACwsLVn31Ra7Lf/zpUspXqPhvlPavcGzjxKTJUwlYtpR1X69mx7YtvF6jBvfu3iU+Ph6tVst834U45PJNEimrYhuosNAQEhMfA5CUlMTF38/nunzqX2fwV8nI0WNxaNqMwE0b+OP3C4RcD6GcdTl6uroxbMQoGjRsWNglvnSKbC+fkuSAa0VDcejlK9b3UJKkNBkoSVKQDJQkKUgGSpIUJAMlSQqSgZIkBclASZKCZKAkSUEyUJKkIBkoSVKQDJQkKUgGSpIUJAMlSQqSgZIkBclASZKCZKAkSUHF4geGkvRvkVcoSVKQDJQkKUgGSpIUJAMlSQqSgZIkBclASZKCZKAkSUEyUJKkIBkoSVKQDJQkKUgGqoD27dtHz5496datG4GBgYVdznNLTEzEzc2NyMjIwi7lpSYDVQAxMTEsXbqUoKAgdu/ezbZt27hx40Zhl5Vvf/zxB++++y7h4eGFXcpLTwaqAE6ePImjoyNWVlaUKlWK7t27c/DgwcIuK9+2b9+Oj48PNi/RoNpFVTEYYOTFiY2NpWLFvwdhs7Gx4eLFi4VY0fNZuHBhYZfwypBXqAJIS0tDpVKZ/hZCZPpbKn5koAqgcuXK3L9/3/T3/fv35cemYk4GqgCcnJw4deoUcXFxJCcnc+jQIdq3z3mkeOnVJ++hCqBSpUpMnTqVIUOGoNfr8fT0xMHBobDLkgqR/Am8JClIfuSTJAXJQEmSgmSgJElBMlCSpCAZKElSkAyUJClIBkqSFPT/wHpIZL6IpnsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,3,1)\n",
    "plt.title(\"Logistic Regression Confusion Matrix\")\n",
    "sns.heatmap(cm_log,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False, annot_kws={\"size\": 24})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "48bd9a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(log, open(\"strokenew.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "864d8d98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
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      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "for i in y_underlog:\n",
    "    print (i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c2919226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9445531637312459"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "960bea73",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0a6cfd4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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